Medical imaging is widely used for diagnosis purposes and a general approach for diagnosis is to detect subtle differences in the composition, morphology or other behavior in organs as can be imaged by different techniques and equipment (ie. modalities) and relate these differences to clinical phenomena of interest.
Image data can be obtained from various sources including for example TI weighted Magnetic Resonance Imaging (“T1w MRI”), T2 weighted MRI (“T2w MRI”), Proton Density weighted MRI (“PD MRI”), Photon Emission Tomography (“PET”), Single Photon Emission Computer Tomography (“SPECT’) and Computer Tomography (“CT’).
Diagnosis of diseases based solely on their imaging characteristics is a challenging task for computer vision. If successful, however, diagnosis approaches can serve multiple purposes such as disease characterization or the morphological assessment of drug effect. Many studies have been conducted to find correlations between image and disease some examples are provided in the following references:
P. A. Freeborough and N. C. Fox, “MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease,” IEEE Trans Med Imaging, vol. 17, pp. 475-9, 1998; J. P. Lerch, J. C. Pruessner, A. Zijdenbos, H. Hampel, S. J. Teipel, and A. C. Evans, “Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy,” Cereb Cortex, vol. 15, pp. 995-1001, 2005; Y. Liu, L. Teverovskiy, O. Carmichael, R. Kikinis, M. Shenton, C. S. Carter, V. A. Stenger, S. Davis, H. Aizenstein, J. T. Becker, O. L. Lopez, and C. C. Meltzer, “Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer Disease Classification,” presented at Medical Image Computing and Computer Assisted Intervention, Saint-Malo, France, 2004; Z. Lao, D. Shen, Z. Xue, B. Karacali, S. M. Resnick, and C. Davatzikos, “Morphological classification of brains via high-dimensional shape transformations and machine learning methods,” Neuroimage, vol. 21, pp. 46-57, 2004; J. G. Csernansky, L. Wang, S. Joshi, J. P. Miller, M. Gado, D. Kido, D. McKeel, J. C. Morris, and M. I. Miller, “Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Dementia of the Alzheimer type,” Neurology, vol. 55, pp. 1636-43, 2000; L. G. Apostolova, R. A. Dutton, I. D. Dinov, K. M. Hayashi, A. W. Toga, J. L. Cummings, and P. M. Thompson, “Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps,” Arch Neurol, vol. 63, pp. 693-9, 2006; P. Golland, W. E. Grimson, M. E. Shenton, and R. Kikinis, “Detection and analysis of statistical differences in anatomical shape,” Med Image Anal, vol. 9, pp. 69-86, 2005; Psychiatry Res. Apr. 30, 2006;146(3):283-7. Epub Mar. 10, 2006. Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus. Fellgiebel A, Dellani P R, Greverus D, Scheurich A, Stoeter P, Muller M J. Surg Radiol Anat. 2006 May; 28(2):150-6. White matter damage of patients with Alzheimer's disease correlated with the decreased cognitive function. Duan J H, Wang H Q, Xu J, Lin X, Chen S Q, Kang Z, Yao Z B. Acta Neurol Scand. 2006 January; 113(1):40-5. Amygdalar volume and psychiatric symptoms in Alzheimer's disease: an MRI analysis. Horinek D, Petrovicky P, Hort J, Krasensky J, Brabec J, Bojar M, Vaneckova M, Seidl Z.Neurol India. 2004 September; 52(3):332-7. T2-weighted MRI in Parkinson's disease; substantia nigra pars compacta hypointensity correlates with the clinical scores. Atasoy H T, Nuyan Q, Tunc T, Yorubulut M, Unal A E, Inan L E. Neuroradiology. 2002 January; 44(1):43-8. Five-year retrospective changes in hippocampal atrophy and cognitive screening test performances in very mild Alzheimer's disease: the Tajiri Project. Yamaguchi S, Meguro K, Shimada M, Ishizaki J, Yamadori A, Sekita Y. Neuroreport. Dec. 3, 2003; 13(17):2299-302. Diffusion tensor in posterior cingulate gyrus: correlation with cognitive decline in Alzheimer's disease. Yoshiura T, Mihara F, Ogomori K, Tanaka A, Kaneko K, Masuda K. Arch Gerontol Geriatr. May 22, 2006; Linear measures of temporal lobe atrophy on brain magnetic resonance imaging (MRI) but not visual rating of white matter changes can help discrimination of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Saka E, Dogan E A, Topcuoglu M A, Senol U, Balkan S. Psychiatry Clin Neurosci. 2006 June; 60(3):319-26. Association of minimal thickness of the medial temporal lobe with hippocampal volume, maximal and minimal hippocampal length: volumetric approach with horizontal magnetic resonance imaging scans for evaluation of a diagnostic marker for neuroimaging of Alzheimer's disease. Uotani C, Sugimori K, Kobayashi K. Cogn Behav Neurol. 2005 September; 18(3):179-84. Predictive model for assessing cognitive impairment by quantitative electroencephalography. Onishi J, Suzuk Y, Yoshiko K, Hibino S, Iguch A.
However, diagnosis approaches, while providing important information on the state of an individual at a point in time, does not in itself provides an assessment of the future evolution of a particular clinical state. Such predictions are only based on the experience of medical practitioners and are a very subjective estimation of the future evolution of a clinical state.